Managers routinely seek to understand firm performance relative to the competitors. Recently, competitive intelligence (CI) has emerged as an important area within business intelligence (BI) where the emphasis is on understanding and measuring a firm's external competitive environment. A requirement of such systems is the availability of the rich data about a firm's competitors, which is typically hard to acquire. This paper proposes a method to incorporate competitive intelligence in BI systems by using less granular and aggregate data, which is usually easier to acquire. We motivate, develop, and validate an approach to infer key competitive measures about customer activities without requiring detailed cross-firm data. Instead, our method derives these competitive measures for online firms from simple "site-centric" data that are commonly available, augmented with aggregate data summaries that may be obtained from syndicated data providers. Based on data provided by comScore Networks, we show empirically that our method performs well in inferring several key diagnostic competitive measures-the penetration, market share, and the share of wallet-for various online retailers.
Due to the vast amount of user data tracked online, the use of data-based analytical methods is becoming increasingly common for e-businesses. Recently the term analytical eCRM has been used to refer to the use of such methods in the online world. A characteristic of most of the current approaches in eCRM is that they use data collected about users' activities at a single site only and, as we argue in this paper, this can present an incomplete picture of user activity. However, it is possible to obtain a complete picture of user activity from across-site data on users. Such data is expensive, but can be obtained by firms directly from their users or from market data vendors. A critical question is whether such data is worth obtaining, an issue that little prior research has addressed. In this paper, using a data mining approach, we present an empirical analysis of the modeling benefits that can be obtained by having complete information. Our results suggest that the magnitudes of gains that can be obtained from complete data range from a few percentage points to 50 percent, depending on the problem for which it is used and the performance metrics considered. Qualitatively we find that variables related to customer loyalty and browsing intensity are particularly important and these variables are difficult to derive from data collected at a single site. More importantly, we find that a firm has to collect a reasonably large amount of complete data before any benefits can be reaped and caution against acquiring too little data.